Notice: On April 23, 2014, Statalist moved from an email list to a forum, based at statalist.org.

[Date Prev][Date Next][Thread Prev][Thread Next][Date Index][Thread Index]

From |
Maarten Buis <maartenlbuis@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: MIXLOGIT: marginal effects |

Date |
Thu, 9 Feb 2012 10:47:19 +0100 |

On Thu, Feb 9, 2012 at 10:11 AM, Brendan Halpin wrote: > To play devil's advocate, let me mention Mood (2010), who argues that > where unobserved heterogeneity makes it invalid to compare log-odds > estimates sizes across samples, the LPM estimate can be more consistent. > > Mood (2010), 'Logistic Regression: Why We Cannot Do What We Think We Can > Do, and What We Can Do About It', European Sociological Review, Volume > 26, Issue 1 Pp. 67-82. To be exact it is not unobserved heterogeneity per se that is causing the problem but the difference in the amount of heterogeneity across groups (heteroskedasticity). As long as you can reasonably believe that the amount heterogeneity is similar, e.g. because you performed a randomized experiment, the odds ratios are perfectly accurate. Anyhow, the characteristic that the estimates are less sensitive to heteroskedasticity is "bought" with the assumptions of linearity in the parameters that people don't like about the linear probability model. So how to choose between the "wrong" LPM and the "wrong" logistic regression? Most importantly, do _not_ go looking for a true model, that is just an oxymoron: a model is and should be a simplification of reality, and a simplification is just another word for being wrong in some useful way. Think of the modeling exercise as taking the information from the observations and using that to build an argument. That argument is just a set of logical statements that lead from the observations to the conclusion. It will involve a couple/many untrue assumptions/simplifications, but as long as they are clearly stated your audience can make up their own mind whether they buy your argument or not and whether they can think of a better argument. Within this framework I am unwilling to exclude the linear probability model in all situations, but I do want to see a reason for using it when it is being used. That reason does not have to be "true", it just has to state the trade-off that has been made. -- Maarten -------------------------- Maarten L. Buis Institut fuer Soziologie Universitaet Tuebingen Wilhelmstrasse 36 72074 Tuebingen Germany http://www.maartenbuis.nl -------------------------- * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**References**:**re: Re: st: MIXLOGIT: marginal effects***From:*Christopher Baum <kit.baum@bc.edu>

**Re: Re: st: MIXLOGIT: marginal effects***From:*Nick Cox <njcoxstata@gmail.com>

**Re: st: MIXLOGIT: marginal effects***From:*brendan.halpin@ul.ie (Brendan Halpin)

- Prev by Date:
**Re: st: Zeros and measures of inequality or concentration** - Next by Date:
**st: Valid and Relevant Instrument** - Previous by thread:
**Re: st: MIXLOGIT: marginal effects** - Next by thread:
**Re: st: MIXLOGIT: marginal effects** - Index(es):